The Impact of Virtual Environments for Future Electric Powered-Mobility Development Using Human-in-the-Loop: Part B - Virtual Testing and Physical Validation

Electric vehicles are increasing in popularity worldwide, and there have been numerous advances in technology to increase the energy efficiency of the vehicle and reduce the range anxiety for the user. For example, the latest electric vehicle (Tesla model S, equipped by 100kWh battery) available in the market in 2019 is able to drive around 375 miles. However, human behavior such as driving strategy is an important issue that impacts on energy optimization and ultimately vehicle range. Human behavior is rather complex and is difficult to replicate with computer algorithms. Therefore, to fully assess the impact of a particular technology, the interactions between humans, vehicle, and the environment need to be examined simultaneously, through a Human-in-the-Loop approach. In this chapter, the results of investigating a human-in-the-loop test platform, which incorporate human-driving behavior and the vehicle characteristics, are presented. In addition, this chapter analyzes a driving strategy, using a Human-in-the-Loop approach, applied to optimizing the energy usage for an electric vehicle competition

Assessing hyper parameter optimization and speedup for convolutional neural networks

The increased processing power of graphical processing units (GPUs) and the availability of large image datasets has fostered a renewed interest in extracting semantic information from images. Promising results for complex image categorization problems have been achieved using deep learning, with neural networks comprised of many layers. Convolutional neural networks (CNN) are one such architecture which provides more opportunities for image classification. Advances in CNN enable the development of training models using large labelled image datasets, but the hyper parameters need to be specified, which is challenging and complex due to the large number of parameters. A substantial amount of computational power and processing time is required to determine the optimal hyper parameters to define a model yielding good results. This article provides a survey of the hyper parameter search and optimization methods for CNN architectures

Safety experiments for small robots investigating the potential of soft materials in mitigating the harm to the head due to impacts

There is a growing interest in social robots to be considered in the therapy of children with autism due to their effectiveness in improving the outcomes. However, children on the spectrum exhibit challenging behaviors that need to be considered when designing robots for them. A child could involuntarily throw a small social robot during meltdown and that could hit another person's head and cause harm (e.g. concussion). In this paper, the application of soft materials is investigated for its potential in attenuating head's linear acceleration upon impact. The thickness and storage modulus of three different soft materials were considered as the control factors while the noise factor was the impact velocity. The design of experiments was based on Taguchi method. A total of 27 experiments were conducted on a developed dummy head setup that reports the linear acceleration of the head. ANOVA tests were performed to analyze the data. The findings showed that the control factors are not statistically significant in attenuating the response. The optimal values of the control factors were identified using the signal-to-noise (S/N) ratio optimization technique. Confirmation runs at the optimal parameters (i.e. thickness of 3 mm and 5 mm) showed a better response as compared to other conditions. Designers of social robots should consider the application of soft materials to their designs as it help in reducing the potential harm to the head

Investigating Decision Support Techniques for Automating Cloud Service Selection

The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud based infrastructure services (e.g. storage, network, CPU).Comment: Accepted by IEEE Cloudcom 2012 - PhD consortium trac

A computational framework for aesthetical navigation in musical search space

Paper presented at 3rd AISB symposium on computational creativity, AISB 2016, 4-6th April, Sheffield. Abstract. This article addresses aspects of an ongoing project in the generation of artificial Persian (-like) music. Liquid Persian Music software (LPM) is a cellular automata based audio generator. In this paper LPM is discussed from the view point of future potentials of algorithmic composition and creativity. Liquid Persian Music is a creative tool, enabling exploration of emergent audio through new dimensions of music composition. Various configurations of the system produce different voices which resemble musical motives in many respects. Aesthetical measurements are determined by Zipf’s law in an evolutionary environment. Arranging these voices together for producing a musical corpus can be considered as a search problem in the LPM outputs space of musical possibilities. On this account, the issues toward defining the search space for LPM is studied throughout this paper